system

Abstract

To provide a system for monitoring the long-time operation of a PC, and for managing the health of a user, and for suppressing excessive labor.SOLUTION: A system according to an embodiment includes a recording part, a monitoring part, an analysis part, a management part, and a cooperation part. The recording unit records the operating time of the PC. The monitoring unit analyzes the data recorded by the recording unit and monitors the long-time operation of the PC. The analysis unit analyzes a facial expression, a viewpoint, a complexion, and a motion speed of the user on the basis of the data monitored by the monitoring unit. The management unit performs suppression of overwork, suppression of overtime, health management, and crisis management based on the data analyzed by the analysis unit. The cooperation unit performs cooperation with a medical institution by early detection of poor physical condition on the basis of the data managed by the management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the challenge of making it difficult to effectively manage overwork and health risks caused by long hours of PC operation.

[0005] The system according to the embodiment aims to monitor long-term operation of a PC, manage the health of users, and prevent overwork. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, a monitoring unit, an analysis unit, a management unit, and a collaboration unit. The recording unit records the operating time of the PC. The monitoring unit analyzes the data recorded by the recording unit and monitors long-term operation of the PC. The analysis unit analyzes the user's facial expression, viewpoint, complexion, and movement speed based on the data monitored by the monitoring unit. The management unit prevents overwork, prevents overtime, manages health, and manages crises based on the data analyzed by the analysis unit. The collaboration unit collaborates with medical institutions to detect poor health early based on the data managed by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the long-term operation of a PC, manage the health of users, and prevent overwork. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management system according to an embodiment of the present invention employs AI to manage employee health. This health management system monitors long-term PC operation and analyzes the user's facial expression, viewpoint, complexion, and movement speed to prevent overwork and overtime, as well as manage health and risk. It also enables early detection of poor health and links with medical institutions. For example, the health management system monitors long-term PC operation. For example, if the PC is continuously running for more than a certain period of time, the AI ​​records the information. Next, the health management system uses a camera to capture the user's face, and the AI ​​analyzes the image to determine the user's facial expression and complexion. The AI ​​also analyzes the user's viewpoint and movement speed to determine the user's level of fatigue and physical condition. Furthermore, if the AI ​​determines that the user is highly fatigued, the health management system displays a message on the PC encouraging the user to take a break. It also enables early detection of poor health and links with medical institutions. For example, if the AI ​​detects that the user is in poor health, it can contact a medical institution. This allows the health management system to efficiently manage employee health, prevent overwork and overtime, and detect poor health early. This allows the health management system to efficiently manage employee health, prevent excessive work and overtime, and detect poor health at an early stage. For example, AI can detect a user's level of fatigue and encourage them to take a break, thereby maintaining employee health. In addition, early detection of poor health allows for smooth coordination with medical institutions and enables prompt response.

[0029] A health management system according to an embodiment includes a recording unit, a monitoring unit, an analysis unit, a management unit, and a linking unit. The recording unit records the operating time of a PC. The operating time of a PC includes, but is not limited to, the time the PC is turned on and the active working time. The recording unit records, for example, the operating time of the PC in minutes. The recording unit can also aggregate the operating time of the PC by day, week, or month. The monitoring unit analyzes the data recorded by the recording unit to monitor the long-term operation of the PC. The long-term operation includes, for example, criteria such as the number of consecutive hours of operation, but is not limited to, the examples. The monitoring unit issues an alert, for example, if the PC operates for four or more consecutive hours. The monitoring unit can also display a warning if the operating time of the PC exceeds eight hours per day. The analysis unit uses a camera to capture a user's face and uses AI to analyze the image to determine the user's facial expression and complexion. The facial expression includes, but is not limited to, classification methods for emotional expressions such as smiling, anger, and sadness. The analysis unit, for example, detects changes in the user's facial color and determines signs of poor health. The analysis unit can also grasp the user's level of fatigue and physical condition by analyzing the user's viewpoint and movement speed. The viewpoint can include, for example, but is not limited to, the direction of gaze and the duration of gaze. The management unit prevents overwork and overtime, manages health, and manages risks. Prevention of overwork can include, for example, but is not limited to, setting a maximum working hour limit and recommending breaks. For example, if the management unit determines that the user is highly fatigued, it can display a message on the PC encouraging the user to take a break. The management unit can also monitor the user's health status and issue a warning if an abnormality is detected. The collaboration unit collaborates with medical institutions to detect poor health early. Collaboration can include, for example, but is not limited to, data sharing methods and collaboration protocols. For example, if the collaboration unit detects the user's poor health, it can contact a medical institution. The collaboration unit can also share the user's health data with medical institutions to support rapid response. As a result, the health management system according to the embodiment can efficiently manage the health of employees, prevent excessive work and overtime, and detect poor health at an early stage.

[0030] When recording the PC's operating time, the recording unit can also simultaneously record the user's work content and application usage status. For example, the recording unit records the type of application used by the user and the usage time. The recording unit can also record the type of work the user is performing (word processing, programming, etc.). The recording unit can also record the usage status of multiple applications that the user has open simultaneously. This allows for more detailed data collection by simultaneously recording the user's work content and application usage status. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's application usage data into a generation AI and have the generation AI analyze the usage status.

[0031] When recording the PC operating time, the recording unit also records the user's break time, making it possible to grasp the ratio between work and break time. For example, the recording unit automatically records the time when the user leaves the PC and recognizes it as a break time. The recording unit can also provide a function that allows the user to manually input the break time when taking a break. The recording unit can also record activities performed by the user during the break (stretching, drinking, etc.). In this way, by recording the user's break time, it is possible to grasp the balance between work and break and to appropriately manage health. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's break time data into the generation AI and have the generation AI analyze the break time.

[0032] When recording the PC operating time, the recording unit can also record the user's input device usage. For example, the recording unit records the time and frequency with which the user uses the keyboard. The recording unit can also record the time and frequency with which the user uses a mouse. If the user uses a touchpad or tablet, the recording unit can also record their usage. This allows detailed work data to be collected by recording the user's input device usage. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's input device usage data into the generation AI and have the generation AI analyze the usage.

[0033] When recording the operating time of the PC, the recording unit can prioritize recording important data based on the user's geographical location information. For example, when the user is in the office, the recording unit can prioritize recording data related to work efficiency. Furthermore, when the user is at home, the recording unit can also prioritize recording data related to health management. Furthermore, when the user is on a business trip, the recording unit can also prioritize recording data related to travel time and rest time. In this way, by taking the user's geographical location information into consideration, highly relevant data can be prioritized and recorded. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's geographical location data to the generation AI and have the generation AI determine the priority of the data.

[0034] When recording the PC operating time, the recording unit can analyze the user's social media activity and record related data. For example, the recording unit can record the user's social media activity time and analyze the correlation with work efficiency. The recording unit can also analyze the content of the user's social media posts and record signs of stress or fatigue. The recording unit can also record the user's social media interaction time and analyze the correlation with break time. In this way, by analyzing the user's social media activity, related data can be recorded and work efficiency and signs of stress can be understood. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's social media data into a generation AI and have the generation AI perform an activity analysis.

[0035] When recording the PC's operating time, the recording unit can customize the recording method by reflecting the user's past feedback. For example, the recording unit customizes the type of data to be recorded based on feedback provided by the user in the past. The recording unit can also improve the recording method by taking into account problems pointed out by the user in the past. The recording unit can also analyze the user's past feedback and set an optimal recording frequency. This allows the recording method to be customized by reflecting the user's past feedback, enabling optimal data collection. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's feedback data into a generation AI and have the generation AI customize the recording method.

[0036] When monitoring long-term PC operation, the monitoring unit can simultaneously monitor the user's work efficiency and productivity. For example, if the user continues working for a long time, the monitoring unit monitors for a decline in work efficiency. The monitoring unit can also tolerate long-term operation if the user maintains high productivity. The monitoring unit can also display a message encouraging the user to take a break if the user is unable to maintain work efficiency. In this way, by simultaneously monitoring the user's work efficiency and productivity, appropriate breaks and work adjustments can be made. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's work efficiency data into the generation AI and have the generation AI perform an efficiency analysis.

[0037] When monitoring the long-term operation of the PC, the monitoring unit can monitor how the user takes breaks and encourage the user to take appropriate breaks. For example, if the user has not taken a break for a certain period of time, the monitoring unit can display a message encouraging the user to take a break. Furthermore, if the user frequently takes short breaks, the monitoring unit can also encourage the user to take longer breaks. Furthermore, if the user is not able to relax during their breaks, the monitoring unit can suggest relaxation methods. In this way, by monitoring how the user takes breaks, appropriate breaks can be encouraged and health management can be achieved. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the monitoring unit can input the user's break time data into the generation AI and have the generation AI execute break suggestions.

[0038] The monitoring unit can monitor the user's health condition and detect abnormalities when monitoring long-term PC operation. For example, if the user's heart rate is abnormally high, the monitoring unit can display a message urging the user to take a break. If the user's blood pressure is abnormally low, the monitoring unit can also suggest collaboration with a medical institution. If the user's health condition is stable, the monitoring unit can continue normal monitoring. This makes it possible to monitor the user's health condition and detect abnormalities early and take appropriate action. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's health data into the generation AI and have the generation AI detect abnormalities.

[0039] When analyzing facial expressions, gaze points, complexion, and movement speed, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. The analysis unit, for example, refers to the user's past facial expression data and analyzes the user's current facial expression. The analysis unit can also refer to the user's past gaze point data and analyze the user's current gaze point. The analysis unit can also refer to the user's past movement speed data and analyze the user's current movement speed. By referring to the user's past data, the accuracy of the analysis can be improved, enabling more accurate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0040] The analysis unit can analyze facial expressions, gaze, complexion, and movement speed based on the user's work content and environment when analyzing the facial expressions, gaze, complexion, and movement speed. For example, when the user is creating a document, the analysis unit analyzes the facial expressions taking into account the work content. Furthermore, when the user is programming, the analysis unit can analyze the gaze taking into account the work content. Furthermore, when the user is in a meeting, the analysis unit can analyze the movement speed taking into account the environment. This enables more appropriate analysis by taking into account the user's work content and environment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's work content data into the generation AI and have the generation AI adjust the analysis.

[0041] The analysis unit can take the user's health condition into consideration when analyzing facial expressions, viewpoints, complexions, and movement speeds. For example, if the user's heart rate is high, the analysis unit analyzes signs of stress or fatigue. The analysis unit can also analyze signs of poor health if the user's blood pressure is low. The analysis unit can also perform normal analysis if the user's health condition is stable. This allows for more appropriate analysis by taking the user's health condition into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's health data into the generation AI and have the generation AI adjust the analysis.

[0042] The management unit can improve the accuracy of management based on the user's past data when preventing excessive work and overtime, managing health, and managing crises. For example, the management unit refers to the user's past working hour data to prevent excessive work. The management unit can also refer to the user's past health data to improve the accuracy of health management. The management unit can also refer to the user's past crisis management data to improve the accuracy of crisis management. By referring to the user's past data, the accuracy of management is improved, enabling more appropriate management. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's past data into the generation AI and have the generation AI improve the accuracy of management.

[0043] The management unit can manage the user's work content and environment while preventing excessive work and overtime, managing their health, and managing crises. For example, when a user is creating a document, the management unit prevents excessive work by taking into account the work content. Furthermore, when a user is programming, the management unit can manage their health by taking into account the work content. Furthermore, when a user is in a meeting, the management unit can perform crisis management by taking into account the environment. This enables more appropriate management by taking into account the user's work content and environment. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's work content data into a generation AI and have the generation AI adjust the management.

[0044] The management unit can take the user's health condition into consideration when preventing overwork and overtime, managing health, and managing crises. For example, if the user's heart rate is high, the management unit can take measures to prevent overwork. The management unit can also take special measures for health management if the user's blood pressure is low. The management unit can also perform normal management if the user's health condition is stable. This enables more appropriate management by taking the user's health condition into consideration. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input the user's health data into the generation AI and have the generation AI adjust the management.

[0045] The linking unit can improve the accuracy of linking with medical institutions due to early detection of poor health by referring to the user's past data. The linking unit, for example, references the user's past health data to improve the accuracy of linking. The linking unit can also reference the user's past linkage data with medical institutions to improve the accuracy of linking. The linking unit can also reference the user's past poor health data to improve the accuracy of linking. By referring to the user's past data, the accuracy of linking is improved, enabling linking with more appropriate medical institutions. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or may be performed without using AI. For example, the linking unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of linking.

[0046] The collaboration unit can perform collaboration with a medical institution in response to early detection of poor health, taking into account the user's work content and environment. For example, when the user is creating a document, the collaboration unit can perform collaboration while taking into account the work content. Furthermore, when the user is programming, the collaboration unit can perform collaboration while taking into account the work content. Furthermore, when the user is in a meeting, the collaboration unit can perform collaboration while taking into account the environment. This enables more appropriate collaboration with a medical institution by taking into account the user's work content and environment. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's work content data into the generation AI and have the generation AI adjust the collaboration.

[0047] The linking unit can link with a medical institution based on the user's health condition when linking with a medical institution due to early detection of poor health. For example, the linking unit can link with a medical institution quickly if the user's heart rate is high. The linking unit can also link with a medical institution quickly if the user's blood pressure is low. The linking unit can also apply a normal linking method if the user's health condition is stable. This enables more appropriate linking with a medical institution by taking the user's health condition into consideration. Some or all of the above-mentioned processing in the linking unit can be performed using AI, for example, or without AI. For example, the linking unit can input the user's health data into the generation AI and have the generation AI adjust the linking.

[0048] The collaboration unit can take the user's geographical location information into consideration when collaborating with a medical institution due to early detection of poor health. For example, when the user is in the office, the collaboration unit prioritizes collaboration with a nearby medical institution. Furthermore, when the user is at home, the collaboration unit can also prioritize collaboration with a medical institution near the user's home. Furthermore, when the user is on a business trip, the collaboration unit can also prioritize collaboration with a medical institution in the user's travel destination. This enables collaboration with a more appropriate medical institution by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input the user's geographical location data into the generation AI and cause the generation AI to adjust the collaboration.

[0049] The linking unit can improve the accuracy of linking with medical institutions due to early detection of poor health by referring to the user's related literature. The linking unit improves the accuracy of linking, for example, based on literature the user has read in the past. The linking unit can also improve the accuracy of linking by referring to research papers related to the user. The linking unit can also improve the accuracy of linking by referring to industry best practices. In this way, by referring to the user's related literature, the accuracy of linking is improved, enabling linking with more appropriate medical institutions. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or without AI. For example, the linking unit can input the user's related literature data into the generation AI and cause the generation AI to improve the accuracy of linking.

[0050] The collaboration unit can take the user's market value into consideration when collaborating with a medical institution due to early detection of poor health. For example, if the user has a high market value, the collaboration unit can quickly collaborate with a medical institution. The collaboration unit can also collaborate to help the user improve their market value. The collaboration unit can also collaborate to help the user maintain their market value. This makes it possible to collaborate with more appropriate medical institutions by taking the user's market value into consideration. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input the user's market value data into the generation AI and have the generation AI adjust the collaboration.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The health management system may further include a meal recording unit that records the user's meal contents. The meal recording unit may record, for example, the types and amounts of food consumed by the user. The meal recording unit may also record the times at which the user ate meals. Furthermore, the meal recording unit may also analyze the user's meal contents and evaluate the nutritional balance. This allows the user's meal contents to be recorded and used to help manage their health.

[0053] The health management system may further include a sleep recording unit that records the user's sleep patterns. The sleep recording unit may record, for example, the time the user goes to bed and the time the user wakes up. The sleep recording unit may also record the user's movements and heart rate during sleep to evaluate the user's sleep quality. The sleep recording unit may also analyze the user's sleep data and provide advice on how to improve the quality of sleep. This allows the user's sleep patterns to be recorded and used to help manage their health.

[0054] The health management system may further include an exercise recording unit that records the amount of exercise performed by the user. The exercise recording unit may record, for example, the type and duration of exercise performed by the user. The exercise recording unit may also record the user's heart rate and calories burned during exercise. Furthermore, the exercise recording unit may analyze the user's exercise data and evaluate the effectiveness of the exercise. This allows the amount of exercise performed by the user to be recorded and used to help manage health.

[0055] The health management system may further include a stress recording unit that records the user's stress level. The stress recording unit may, for example, record the user's heart rate and blood pressure and evaluate the stress level. The stress recording unit may also record events and situations related to the user's stress. Furthermore, the stress recording unit may analyze the user's stress data and provide advice for reducing stress. In this way, the user's stress level may be recorded and used to help manage their health.

[0056] The health management system may further include a moisture recording unit that records the user's moisture intake. The moisture recording unit may record, for example, the type and amount of moisture consumed by the user. The moisture recording unit may also record the time the user consumes moisture. Furthermore, the moisture recording unit may analyze the user's moisture intake data and recommend an appropriate amount of moisture intake. This allows the user's moisture intake to be recorded and used to help manage their health.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The recorder records the PC's operating time. PC operating time includes, for example, the time the PC is turned on and the time it is actively working. The recorder records the PC's operating time in minutes and can also aggregate it by day, week, or month. Step 2: The monitoring unit analyzes the data recorded by the recording unit and monitors long-term PC operation. For example, it issues an alert if the PC is running for more than four consecutive hours, and displays a warning if it runs for more than eight hours in a day. Step 3: The analysis unit uses a camera to take a picture of the user's face, and the AI ​​analyzes the image to determine the user's facial expression, viewpoint, complexion, and movement speed, thereby determining the user's level of fatigue and physical condition. Step 4: Based on the data analyzed by the analysis unit, the management unit prevents overwork, prevents overtime work, manages health, and manages risks. For example, if it determines that the user is highly fatigued, it displays a message on the PC urging them to take a break. Step 5: Based on the data managed by the management unit, the collaboration unit collaborates with medical institutions to detect early signs of poor health. For example, if the collaboration unit detects that the user is in poor health, it contacts the medical institution and shares the health data.

[0059] (Example 2) A health management system according to an embodiment of the present invention employs AI to manage employee health. This health management system monitors long-term PC operation and analyzes the user's facial expression, viewpoint, complexion, and movement speed to prevent overwork and overtime, as well as manage health and risk. It also enables early detection of poor health and links with medical institutions. For example, the health management system monitors long-term PC operation. For example, if the PC is continuously running for more than a certain period of time, the AI ​​records the information. Next, the health management system uses a camera to capture the user's face, and the AI ​​analyzes the image to determine the user's facial expression and complexion. The AI ​​also analyzes the user's viewpoint and movement speed to determine the user's level of fatigue and physical condition. Furthermore, if the AI ​​determines that the user is highly fatigued, the health management system displays a message on the PC encouraging the user to take a break. It also enables early detection of poor health and links with medical institutions. For example, if the AI ​​detects that the user is in poor health, it can contact a medical institution. This allows the health management system to efficiently manage employee health, prevent overwork and overtime, and detect poor health early. This allows the health management system to efficiently manage employee health, prevent excessive work and overtime, and detect poor health at an early stage. For example, AI can detect a user's level of fatigue and encourage them to take a break, thereby maintaining employee health. In addition, early detection of poor health allows for smooth coordination with medical institutions and enables prompt response.

[0060] A health management system according to an embodiment includes a recording unit, a monitoring unit, an analysis unit, a management unit, and a linking unit. The recording unit records the operating time of a PC. The operating time of a PC includes, but is not limited to, the time the PC is turned on and the active working time. The recording unit records, for example, the operating time of the PC in minutes. The recording unit can also aggregate the operating time of the PC by day, week, or month. The monitoring unit analyzes the data recorded by the recording unit to monitor the long-term operation of the PC. The long-term operation includes, for example, criteria such as the number of consecutive hours of operation, but is not limited to, the examples. The monitoring unit issues an alert, for example, if the PC operates for four or more consecutive hours. The monitoring unit can also display a warning if the operating time of the PC exceeds eight hours per day. The analysis unit uses a camera to capture a user's face and uses AI to analyze the image to determine the user's facial expression and complexion. The facial expression includes, but is not limited to, classification methods for emotional expressions such as smiling, anger, and sadness. The analysis unit, for example, detects changes in the user's facial color and determines signs of poor health. The analysis unit can also grasp the user's level of fatigue and physical condition by analyzing the user's viewpoint and movement speed. The viewpoint can include, for example, but is not limited to, the direction of gaze and the duration of gaze. The management unit prevents overwork and overtime, manages health, and manages risks. Prevention of overwork can include, for example, but is not limited to, setting a maximum working hour limit and recommending breaks. For example, if the management unit determines that the user is highly fatigued, it can display a message on the PC encouraging the user to take a break. The management unit can also monitor the user's health status and issue a warning if an abnormality is detected. The collaboration unit collaborates with medical institutions to detect poor health early. Collaboration can include, for example, but is not limited to, data sharing methods and collaboration protocols. For example, if the collaboration unit detects the user's poor health, it can contact a medical institution. The collaboration unit can also share the user's health data with medical institutions to support rapid response. As a result, the health management system according to the embodiment can efficiently manage the health of employees, prevent excessive work and overtime, and detect poor health at an early stage.

[0061] The recording unit can estimate the user's emotions and adjust the recording frequency of the PC's operating time based on the estimated user emotions. For example, when the user is stressed, the recording unit can increase the recording frequency to collect detailed data. Furthermore, when the user is relaxed, the recording unit can also reduce the recording frequency to collect the minimum necessary data. Furthermore, when the user is tired, the recording unit can set the recording frequency to a medium level to collect appropriate data. This allows for more appropriate data collection by adjusting the recording frequency based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or without an AI. For example, the recording unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0062] When recording the PC's operating time, the recording unit can also simultaneously record the user's work content and application usage status. For example, the recording unit records the type of application used by the user and the usage time. The recording unit can also record the type of work the user is performing (word processing, programming, etc.). The recording unit can also record the usage status of multiple applications that the user has open simultaneously. This allows for more detailed data collection by simultaneously recording the user's work content and application usage status. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's application usage data into a generation AI and have the generation AI analyze the usage status.

[0063] When recording the PC operating time, the recording unit also records the user's break time, making it possible to grasp the ratio between work and break time. For example, the recording unit automatically records the time when the user leaves the PC and recognizes it as a break time. The recording unit can also provide a function that allows the user to manually input the break time when taking a break. The recording unit can also record activities performed by the user during the break (stretching, drinking, etc.). In this way, by recording the user's break time, it is possible to grasp the balance between work and break and to appropriately manage health. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's break time data into the generation AI and have the generation AI analyze the break time.

[0064] When recording the PC operating time, the recording unit can also record the user's input device usage. For example, the recording unit records the time and frequency with which the user uses the keyboard. The recording unit can also record the time and frequency with which the user uses a mouse. If the user uses a touchpad or tablet, the recording unit can also record their usage. This allows detailed work data to be collected by recording the user's input device usage. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's input device usage data into the generation AI and have the generation AI analyze the usage.

[0065] The recording unit can estimate the user's emotions and determine the priority of data to be recorded based on the estimated user emotions. For example, if the user is feeling stressed, the recording unit can prioritize recording health-related data. Furthermore, if the user is relaxed, the recording unit can prioritize recording data related to work efficiency. Furthermore, if the user is tired, the recording unit can prioritize recording data related to rest times. Thus, by determining the priority of data to be recorded based on the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or without an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0066] When recording the operating time of the PC, the recording unit can prioritize recording important data based on the user's geographical location information. For example, when the user is in the office, the recording unit can prioritize recording data related to work efficiency. Furthermore, when the user is at home, the recording unit can also prioritize recording data related to health management. Furthermore, when the user is on a business trip, the recording unit can also prioritize recording data related to travel time and rest time. In this way, by taking the user's geographical location information into consideration, highly relevant data can be prioritized and recorded. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's geographical location data to the generation AI and have the generation AI determine the priority of the data.

[0067] When recording the PC operating time, the recording unit can analyze the user's social media activity and record related data. For example, the recording unit can record the user's social media activity time and analyze the correlation with work efficiency. The recording unit can also analyze the content of the user's social media posts and record signs of stress or fatigue. The recording unit can also record the user's social media interaction time and analyze the correlation with break time. In this way, by analyzing the user's social media activity, related data can be recorded and work efficiency and signs of stress can be understood. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's social media data into a generation AI and have the generation AI perform an activity analysis.

[0068] When recording the PC's operating time, the recording unit can customize the recording method by reflecting the user's past feedback. For example, the recording unit customizes the type of data to be recorded based on feedback provided by the user in the past. The recording unit can also improve the recording method by taking into account problems pointed out by the user in the past. The recording unit can also analyze the user's past feedback and set an optimal recording frequency. This allows the recording method to be customized by reflecting the user's past feedback, enabling optimal data collection. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's feedback data into a generation AI and have the generation AI customize the recording method.

[0069] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria for long PC operation based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can set monitoring criteria to encourage the user to take a short break. The monitoring unit can also apply normal monitoring criteria when the user is relaxed. The monitoring unit can also set strict monitoring criteria to avoid long operation when the user is tired. This enables more appropriate monitoring by adjusting the monitoring criteria based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring criteria.

[0070] When monitoring long-term PC operation, the monitoring unit can simultaneously monitor the user's work efficiency and productivity. For example, if the user continues working for a long time, the monitoring unit monitors for a decline in work efficiency. The monitoring unit can also tolerate long-term operation if the user maintains high productivity. The monitoring unit can also display a message encouraging the user to take a break if the user is unable to maintain work efficiency. In this way, by simultaneously monitoring the user's work efficiency and productivity, appropriate breaks and work adjustments can be made. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's work efficiency data into the generation AI and have the generation AI perform an efficiency analysis.

[0071] When monitoring the long-term operation of the PC, the monitoring unit can monitor how the user takes breaks and encourage the user to take appropriate breaks. For example, if the user has not taken a break for a certain period of time, the monitoring unit can display a message encouraging the user to take a break. Furthermore, if the user frequently takes short breaks, the monitoring unit can also encourage the user to take longer breaks. Furthermore, if the user is not able to relax during their breaks, the monitoring unit can suggest relaxation methods. In this way, by monitoring how the user takes breaks, appropriate breaks can be encouraged and health management can be achieved. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the monitoring unit can input the user's break time data into the generation AI and have the generation AI execute break suggestions.

[0072] The monitoring unit can monitor the user's health condition and detect abnormalities when monitoring long-term PC operation. For example, if the user's heart rate is abnormally high, the monitoring unit can display a message urging the user to take a break. If the user's blood pressure is abnormally low, the monitoring unit can also suggest collaboration with a medical institution. If the user's health condition is stable, the monitoring unit can continue normal monitoring. This makes it possible to monitor the user's health condition and detect abnormalities early and take appropriate action. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's health data into the generation AI and have the generation AI detect abnormalities.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis method of facial expressions, gaze, facial color, and movement speed based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform an analysis at a leisurely pace. If the user is in a hurry, the analysis unit can also perform a quick analysis. If the user is excited, the analysis unit can also perform a detailed analysis. This allows for more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method.

[0074] When analyzing facial expressions, gaze points, complexion, and movement speed, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. The analysis unit, for example, refers to the user's past facial expression data and analyzes the user's current facial expression. The analysis unit can also refer to the user's past gaze point data and analyze the user's current gaze point. The analysis unit can also refer to the user's past movement speed data and analyze the user's current movement speed. By referring to the user's past data, the accuracy of the analysis can be improved, enabling more accurate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0075] The analysis unit can analyze facial expressions, gaze, complexion, and movement speed based on the user's work content and environment when analyzing the facial expressions, gaze, complexion, and movement speed. For example, when the user is creating a document, the analysis unit analyzes the facial expressions taking into account the work content. Furthermore, when the user is programming, the analysis unit can analyze the gaze taking into account the work content. Furthermore, when the user is in a meeting, the analysis unit can analyze the movement speed taking into account the environment. This enables more appropriate analysis by taking into account the user's work content and environment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's work content data into the generation AI and have the generation AI adjust the analysis.

[0076] The analysis unit can take the user's health condition into consideration when analyzing facial expressions, viewpoints, complexions, and movement speeds. For example, if the user's heart rate is high, the analysis unit analyzes signs of stress or fatigue. The analysis unit can also analyze signs of poor health if the user's blood pressure is low. The analysis unit can also perform normal analysis if the user's health condition is stable. This allows for more appropriate analysis by taking the user's health condition into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's health data into the generation AI and have the generation AI adjust the analysis.

[0077] The management unit can estimate the user's emotions and adjust methods for preventing overwork and overtime, health management, and crisis management based on the estimated user's emotions. For example, if the user is feeling stressed, the management unit can set strict standards to prevent overwork. The management unit can also apply normal standards if the user is relaxed. The management unit can also take special measures for health management if the user is tired. This enables more appropriate management by adjusting the management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the management method.

[0078] The management unit can improve the accuracy of management based on the user's past data when preventing excessive work and overtime, managing health, and managing crises. For example, the management unit refers to the user's past working hour data to prevent excessive work. The management unit can also refer to the user's past health data to improve the accuracy of health management. The management unit can also refer to the user's past crisis management data to improve the accuracy of crisis management. By referring to the user's past data, the accuracy of management is improved, enabling more appropriate management. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's past data into the generation AI and have the generation AI improve the accuracy of management.

[0079] The management unit can manage the user's work content and environment while preventing excessive work and overtime, managing their health, and managing crises. For example, when a user is creating a document, the management unit prevents excessive work by taking into account the work content. Furthermore, when a user is programming, the management unit can manage their health by taking into account the work content. Furthermore, when a user is in a meeting, the management unit can perform crisis management by taking into account the environment. This enables more appropriate management by taking into account the user's work content and environment. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's work content data into a generation AI and have the generation AI adjust the management.

[0080] The management unit can take the user's health condition into consideration when preventing overwork and overtime, managing health, and managing crises. For example, if the user's heart rate is high, the management unit can take measures to prevent overwork. The management unit can also take special measures for health management if the user's blood pressure is low. The management unit can also perform normal management if the user's health condition is stable. This enables more appropriate management by taking the user's health condition into consideration. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input the user's health data into the generation AI and have the generation AI adjust the management.

[0081] The collaboration unit can estimate the user's emotions and optimize a collaboration method with a medical institution for early detection of poor health based on the estimated user emotions. For example, if the user is feeling stressed, the collaboration unit can collaborate with a medical institution early. If the user is relaxed, the collaboration unit can also apply a normal collaboration method. If the user is tired, the collaboration unit can also quickly collaborate with a medical institution. This enables more appropriate collaboration with a medical institution by adjusting the collaboration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collaboration unit can be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's emotion data into the generation AI and cause the generation AI to optimize the collaboration method.

[0082] The linking unit can improve the accuracy of linking with medical institutions due to early detection of poor health by referring to the user's past data. The linking unit, for example, references the user's past health data to improve the accuracy of linking. The linking unit can also reference the user's past linkage data with medical institutions to improve the accuracy of linking. The linking unit can also reference the user's past poor health data to improve the accuracy of linking. By referring to the user's past data, the accuracy of linking is improved, enabling linking with more appropriate medical institutions. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or may be performed without using AI. For example, the linking unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of linking.

[0083] The collaboration unit can perform collaboration with a medical institution in response to early detection of poor health, taking into account the user's work content and environment. For example, when the user is creating a document, the collaboration unit can perform collaboration while taking into account the work content. Furthermore, when the user is programming, the collaboration unit can perform collaboration while taking into account the work content. Furthermore, when the user is in a meeting, the collaboration unit can perform collaboration while taking into account the environment. This enables more appropriate collaboration with a medical institution by taking into account the user's work content and environment. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's work content data into the generation AI and have the generation AI adjust the collaboration.

[0084] The linking unit can link with a medical institution based on the user's health condition when linking with a medical institution due to early detection of poor health. For example, the linking unit can link with a medical institution quickly if the user's heart rate is high. The linking unit can also link with a medical institution quickly if the user's blood pressure is low. The linking unit can also apply a normal linking method if the user's health condition is stable. This enables more appropriate linking with a medical institution by taking the user's health condition into consideration. Some or all of the above-mentioned processing in the linking unit can be performed using AI, for example, or without AI. For example, the linking unit can input the user's health data into the generation AI and have the generation AI adjust the linking.

[0085] The linking unit can estimate the user's emotions and adjust the display method of the linking results based on the estimated user emotions. For example, if the user is nervous, the linking unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the linking unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the linking unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the linking unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0086] The collaboration unit can take the user's geographical location information into consideration when collaborating with a medical institution due to early detection of poor health. For example, when the user is in the office, the collaboration unit prioritizes collaboration with a nearby medical institution. Furthermore, when the user is at home, the collaboration unit can also prioritize collaboration with a medical institution near the user's home. Furthermore, when the user is on a business trip, the collaboration unit can also prioritize collaboration with a medical institution in the user's travel destination. This enables collaboration with a more appropriate medical institution by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input the user's geographical location data into the generation AI and cause the generation AI to adjust the collaboration.

[0087] The linking unit can improve the accuracy of linking with medical institutions due to early detection of poor health by referring to the user's related literature. The linking unit improves the accuracy of linking, for example, based on literature the user has read in the past. The linking unit can also improve the accuracy of linking by referring to research papers related to the user. The linking unit can also improve the accuracy of linking by referring to industry best practices. In this way, by referring to the user's related literature, the accuracy of linking is improved, enabling linking with more appropriate medical institutions. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or without AI. For example, the linking unit can input the user's related literature data into the generation AI and cause the generation AI to improve the accuracy of linking.

[0088] The collaboration unit can take the user's market value into consideration when collaborating with a medical institution due to early detection of poor health. For example, if the user has a high market value, the collaboration unit can quickly collaborate with a medical institution. The collaboration unit can also collaborate to help the user improve their market value. The collaboration unit can also collaborate to help the user maintain their market value. This makes it possible to collaborate with more appropriate medical institutions by taking the user's market value into consideration. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input the user's market value data into the generation AI and have the generation AI adjust the collaboration. === Hard Collateral 1-1 === Each of the multiple elements, including the recording unit, monitoring unit, analysis unit, management unit, and linking unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recording unit can record the operating time of the PC using the control unit 46A of the smart device 14. The monitoring unit can monitor the long-term operation of the PC using the specific processing unit 290 of the data processing device 12. The analysis unit can photograph the user's face using the camera 42 of the smart device 14 and analyze the image using the specific processing unit 290 of the data processing device 12. The management unit can prevent overwork and overtime, manage health, and manage risks using the specific processing unit 290 of the data processing device 12. The linking unit can coordinate with medical institutions by early detection of poor health using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the recording unit, monitoring unit, analysis unit, management unit, and linking unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recording unit can record the operating time of the PC using the control unit 46A of the smart glasses 214. The monitoring unit can monitor the long-term operation of the PC using the specific processing unit 290 of the data processing device 12. The analysis unit can photograph the user's face using the camera 42 of the smart glasses 214 and analyze the image using the specific processing unit 290 of the data processing device 12. The management unit can prevent excessive work and overtime, manage health, and manage risks using the specific processing unit 290 of the data processing device 12. The linking unit can coordinate with medical institutions by early detection of poor health using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the recording unit, monitoring unit, analysis unit, management unit, and linking unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the recording unit can record the operating time of the PC using the control unit 46A of the headset-type terminal 314. The monitoring unit can monitor long-term operation of the PC using the specific processing unit 290 of the data processing device 12. The analysis unit can photograph the user's face using the camera 42 of the headset-type terminal 314 and analyze the image using the specific processing unit 290 of the data processing device 12. The management unit can prevent overwork and overtime, manage health, and manage risks using the specific processing unit 290 of the data processing device 12. The linking unit can coordinate with medical institutions by early detection of poor health using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the recording unit, monitoring unit, analysis unit, management unit, and collaboration unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recording unit can record the operating time of the PC using the control unit 46A of the robot 414. The monitoring unit can monitor the long-term operation of the PC using the specific processing unit 290 of the data processing device 12. The analysis unit can photograph the user's face using the camera 42 of the robot 414 and analyze the image using the specific processing unit 290 of the data processing device 12. The management unit can prevent overwork and overtime, manage health, and manage risks using the specific processing unit 290 of the data processing device 12. The collaboration unit can collaborate with medical institutions by early detection of poor health using the specific processing unit 290 of the data processing device 12.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The health management system may further include a meal recording unit that records the user's meal contents. The meal recording unit may record, for example, the types and amounts of food consumed by the user. The meal recording unit may also record the times at which the user ate meals. Furthermore, the meal recording unit may also analyze the user's meal contents and evaluate the nutritional balance. This allows the user's meal contents to be recorded and used to help manage their health.

[0091] The health management system may further include a sleep recording unit that records the user's sleep patterns. The sleep recording unit may record, for example, the time the user goes to bed and the time the user wakes up. The sleep recording unit may also record the user's movements and heart rate during sleep to evaluate the user's sleep quality. The sleep recording unit may also analyze the user's sleep data and provide advice on how to improve the quality of sleep. This allows the user's sleep patterns to be recorded and used to help manage their health.

[0092] The health management system may further include an exercise recording unit that records the amount of exercise performed by the user. The exercise recording unit may record, for example, the type and duration of exercise performed by the user. The exercise recording unit may also record the user's heart rate and calories burned during exercise. Furthermore, the exercise recording unit may analyze the user's exercise data and evaluate the effectiveness of the exercise. This allows the amount of exercise performed by the user to be recorded and used to help manage health.

[0093] The health management system may further include a stress recording unit that records the user's stress level. The stress recording unit may, for example, record the user's heart rate and blood pressure and evaluate the stress level. The stress recording unit may also record events and situations related to the user's stress. Furthermore, the stress recording unit may analyze the user's stress data and provide advice for reducing stress. In this way, the user's stress level may be recorded and used to help manage their health.

[0094] The health management system may further include a moisture recording unit that records the user's moisture intake. The moisture recording unit may record, for example, the type and amount of moisture consumed by the user. The moisture recording unit may also record the time the user consumes moisture. Furthermore, the moisture recording unit may analyze the user's moisture intake data and recommend an appropriate amount of moisture intake. This allows the user's moisture intake to be recorded and used to help manage their health.

[0095] The health management system can further estimate the user's emotions and provide dietary advice based on the estimated user's emotions. For example, if the user is feeling stressed, it can recommend meals that help reduce stress. If the user is relaxed, it can also recommend meals that help maintain relaxation. Furthermore, if the user is tired, it can also recommend meals that help recover from fatigue. In this way, dietary advice can be provided based on the user's emotions, which can be useful for health management.

[0096] The health management system can further estimate the user's emotions and provide advice to improve sleep quality based on the estimated user's emotions. For example, if the user is feeling stressed, it can recommend relaxation methods to reduce stress. Also, if the user is relaxed, it can recommend a sleep environment to maintain relaxation. Furthermore, if the user is tired, it can recommend a sleep method to recover from fatigue. In this way, advice to improve sleep quality based on the user's emotions can be provided, which can be useful for health management.

[0097] The health management system can further estimate the user's emotions and provide exercise advice based on the estimated user emotions. For example, if the user is feeling stressed, it can recommend exercises that will help reduce stress. If the user is relaxed, it can also recommend exercises that will help maintain relaxation. Furthermore, if the user is tired, it can also recommend exercises that will help recover from fatigue. In this way, it is possible to provide exercise advice based on the user's emotions and use it to help manage health.

[0098] The health management system can further estimate the user's emotions and provide advice for reducing stress based on the estimated user emotions. For example, if the user is feeling stressed, it can recommend relaxation methods for reducing stress. If the user is relaxed, it can also recommend methods for maintaining relaxation. Furthermore, if the user is tired, it can also recommend relaxation methods for recovering from fatigue. In this way, advice for reducing stress based on the user's emotions can be provided, which can be useful for health management.

[0099] The health management system can further estimate the user's emotions and provide advice on hydration based on the estimated user's emotions. For example, if the user is feeling stressed, it can recommend a drink that helps relieve stress. If the user is relaxed, it can also recommend a drink that helps maintain relaxation. Furthermore, if the user is tired, it can also recommend a drink that helps recover from fatigue. In this way, it is possible to provide advice on hydration based on the user's emotions and to help with health management.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The recorder records the PC's operating time. PC operating time includes, for example, the time the PC is turned on and the time it is actively working. The recorder records the PC's operating time in minutes and can also aggregate it by day, week, or month. Step 2: The monitoring unit analyzes the data recorded by the recording unit and monitors long-term PC operation. For example, it issues an alert if the PC is running for more than four consecutive hours, and displays a warning if it runs for more than eight hours in a day. Step 3: The analysis unit uses a camera to take a picture of the user's face, and the AI ​​analyzes the image to determine the user's facial expression, viewpoint, complexion, and movement speed, thereby determining the user's level of fatigue and physical condition. Step 4: Based on the data analyzed by the analysis unit, the management unit prevents overwork, prevents overtime work, manages health, and manages risks. For example, if it determines that the user is highly fatigued, it displays a message on the PC urging them to take a break. Step 5: Based on the data managed by the management unit, the collaboration unit collaborates with medical institutions to detect early signs of poor health. For example, if the collaboration unit detects that the user is in poor health, it contacts the medical institution and shares the health data.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A recording unit that records the PC's operating time, a monitoring unit that analyzes the data recorded by the recording unit and monitors long-term operation of the PC; an analysis unit that analyzes the user's facial expression, viewpoint, complexion, and movement speed based on the data monitored by the monitoring unit; a management department that prevents overwork, prevents overtime work, manages health, and manages risks based on the data analyzed by the analysis department; A linking unit that links with medical institutions by detecting poor health at an early stage based on the data managed by the management unit. A system characterized by:

2. The recording unit Inferring user emotions and adjusting the frequency of recording PC operating time based on the estimated user emotions 2. The system of claim 1.

3. The recording unit When recording PC operating time, simultaneously record user activity and application usage.

2. The system of claim 1.

4. The recording unit When recording PC operating time, record user break times as well to understand the ratio of work time to break time.

2. The system of claim 1.

5. The recording unit When recording PC operating time, also record the user's input device usage 2. The system of claim 1.

6. The recording unit Estimate the user's emotions and determine the priority of data to be recorded based on the estimated user emotions.

2. The system of claim 1.

7. The recording unit When recording PC uptime, prioritize important data based on the user's geographic location.

2. The system of claim 1.

8. The recording unit Analyze users' social media activity and record relevant data when recording PC uptime 2. The system of claim 1.

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